Looking at the paper, the core message is 'that even scientists harbor the illusion of understanding more than they actually do'.
In reality, science operates much like a mental model. The paper argues that just because a model predicts future values more accurately, it doesn't mean the model explains the actual causal structure. Yet, the fact that outcomes fall within the predicted range reinforces the illusion that one has truly 'understood' it.
This reminds me of the statistician's aphorism: 'All models are wrong, but some are useful.' Science itself, in a way, is a mental model—a simplification created for humans because the world is a complex system that is cognitively impossible to fully comprehend. Within that framework, certain facts reinforce the mental model, while others weaken it. While mental models vary from person to person, in a broad sense, we are commonly taught to view the macroscopic world through the Newtonian model and the microscopic world through the quantum mechanics model.
Reading this makes me reconsider what 'understanding' truly means. I believe the starting point of genuine understanding is acknowledging that perfect prediction is ultimately impossible, and that when viewing the world through our mental models, what matters is defining what we consider to be acceptable 'lossy information' (or information we can afford to lose)
This is kind of interesting, but I predict that it pleases almost nobody. Philosophy of science types will be kind of annoyed at the preoccupation with statistics, ML people will be annoyed at too much philosophy of science, etc.
I totally support a goal to get those groups talking more but something tighter is probably better. And why isn't it tighter? Without big original contributions, the goal does seem to be a survey
This is a classic case of overthinking.
Induction should not yield new knowledge because nothing new is discovered, but it does.
Deduction likewise also cannot establish new knowledge, yet it does.
Empirical science is flawed on extremely many levels but it works because on average, over time, many converging observations can build refined and accurate causal theories.
It’s a matter of practicality that things cannot be proven fully. Judging from the state of modern medicine, engineering and the sciences, the system works ok regardless
"the sciences" is very broad. in biology there are established methods for establishing causality (i.e. Koch's postulates, etc), and even then conclusions are generally qualified. not sure about the other fields, but I wish they had more concrete and recent examples of what they are talking about. this was painful to even skim.
also for some reason i cant click on anyting on the site or select text?
What is a model anyways? There are so many answers to say you that. The models are almost the same models, but at a different abstraction away from the original experienced in reality.
Looking at the paper, the core message is 'that even scientists harbor the illusion of understanding more than they actually do'.
In reality, science operates much like a mental model. The paper argues that just because a model predicts future values more accurately, it doesn't mean the model explains the actual causal structure. Yet, the fact that outcomes fall within the predicted range reinforces the illusion that one has truly 'understood' it.
This reminds me of the statistician's aphorism: 'All models are wrong, but some are useful.' Science itself, in a way, is a mental model—a simplification created for humans because the world is a complex system that is cognitively impossible to fully comprehend. Within that framework, certain facts reinforce the mental model, while others weaken it. While mental models vary from person to person, in a broad sense, we are commonly taught to view the macroscopic world through the Newtonian model and the microscopic world through the quantum mechanics model.
Reading this makes me reconsider what 'understanding' truly means. I believe the starting point of genuine understanding is acknowledging that perfect prediction is ultimately impossible, and that when viewing the world through our mental models, what matters is defining what we consider to be acceptable 'lossy information' (or information we can afford to lose)
> This reminds me of the statistician's aphorism: 'All models are wrong, but some are useful.'
It reminded the authors of this too, since they quote and source it
This is kind of interesting, but I predict that it pleases almost nobody. Philosophy of science types will be kind of annoyed at the preoccupation with statistics, ML people will be annoyed at too much philosophy of science, etc.
I totally support a goal to get those groups talking more but something tighter is probably better. And why isn't it tighter? Without big original contributions, the goal does seem to be a survey
However, for a freelance programmer like me who has to model the world the client wants, this might actually be a useful problem. LOL
This is a classic case of overthinking. Induction should not yield new knowledge because nothing new is discovered, but it does. Deduction likewise also cannot establish new knowledge, yet it does. Empirical science is flawed on extremely many levels but it works because on average, over time, many converging observations can build refined and accurate causal theories. It’s a matter of practicality that things cannot be proven fully. Judging from the state of modern medicine, engineering and the sciences, the system works ok regardless
this is extremely long and repetitive.
"the sciences" is very broad. in biology there are established methods for establishing causality (i.e. Koch's postulates, etc), and even then conclusions are generally qualified. not sure about the other fields, but I wish they had more concrete and recent examples of what they are talking about. this was painful to even skim.
also for some reason i cant click on anyting on the site or select text?
What is a model anyways? There are so many answers to say you that. The models are almost the same models, but at a different abstraction away from the original experienced in reality.